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An enhanced component connection method for conversion of fault trees to binary decision diagrams

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  • Remenyte-Prescott, R.
  • Andrews, J.D.

Abstract

Fault tree analysis (FTA) is widely applied to assess the failure probability of industrial systems. Many computer packages are available, which are based on conventional kinetic tree theory methods. When dealing with large (possibly non-coherent) fault trees, the limitations of the technique in terms of accuracy of the solutions and the efficiency of the processing time become apparent. Over recent years, the binary decision diagram (BDD) method has been developed that solves fault trees and overcomes the disadvantages of the conventional FTA approach. First of all, a fault tree for a particular system failure mode is constructed and then converted to a BDD for analysis. This paper analyses alternative methods for the fault tree to BDD conversion process.

Suggested Citation

  • Remenyte-Prescott, R. & Andrews, J.D., 2008. "An enhanced component connection method for conversion of fault trees to binary decision diagrams," Reliability Engineering and System Safety, Elsevier, vol. 93(10), pages 1543-1550.
    • Handle: RePEc:eee:reensy:v:93:y:2008:i:10:p:1543-1550
    DOI: 10.1016/j.ress.2007.09.001
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    References listed on IDEAS

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    1. R Remenyte & J. D. Andrews, 2006. "Qualitative Analysis of Complex Modularized Fault Trees Using Binary Decision Diagrams," Journal of Risk and Reliability, , vol. 220(1), pages 45-53, June.
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    Cited by:

    1. Nicolae Brînzei & Jean-François Aubry, 2018. "Graphs models and algorithms for reliability assessment of coherent and non-coherent systems," Journal of Risk and Reliability, , vol. 232(2), pages 201-215, April.
    2. Li, Shumin & Si, Shubin & Dui, Hongyan & Cai, Zhiqiang & Sun, Shudong, 2014. "A novel decision diagrams extension method," Reliability Engineering and System Safety, Elsevier, vol. 126(C), pages 107-115.
    3. Zywiec, William J. & Mazzuchi, Thomas A. & Sarkani, Shahram, 2021. "Analysis of process criticality accident risk using a metamodel-driven Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 207(C).

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